Merge pull request #3253 from LDOUBLEV/fix_ci

Fix ci

deploy/slim/quantization/export_model.py

@@ -110,10 +110,12 @@ def main():
     # build dataloader
     valid_dataloader = build_dataloader(config, 'Eval', device, logger)
 
-    # start eval
+    use_srn = config['Architecture']['algorithm'] == "SRN"
     model_type = config['Architecture']['model_type']
-    metric = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, model_type)
+    # start eval
+    metirc = program.eval(model, valid_dataloader, post_process_class,
+                          eval_class, model_type, use_srn)
+
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

test/ocr_det_params.txt

 model_name:ocr_det
 python:python3.7
-gpu_list:-1|0|0,1
-Global.auto_cast:False|True
+gpu_list:0|0,1
+Global.auto_cast:False
 Global.epoch_num:10
 Global.save_model_dir:./output/
 Global.save_inference_dir:./output/
@@ -9,7 +9,7 @@ Train.loader.batch_size_per_card:
 Global.use_gpu
 Global.pretrained_model
 
-trainer:norm|pact|fpgm
+trainer:norm|pact
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
 fpgm_train:null

test/prepare.sh

@@ -94,7 +94,7 @@ for train_model in ${train_model_list[*]}; do
     # eval 
     for slim_trainer in ${trainer_list[*]}; do 
         if [ ${slim_trainer} = "norm" ]; then
-            if [ ${model_name} = "det" ]; then
+            if [ ${model_name} = "ocr_det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -104,7 +104,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi 
         elif [ ${slim_trainer} = "pact" ]; then
-            if [ ${model_name} = "det" ]; then
+            if [ ${model_name} = "ocr_det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -114,7 +114,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi
         elif [ ${slim_trainer} = "distill" ]; then
-            if [ ${model_name} = "det" ]; then
+            if [ ${model_name} = "ocr_det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -124,7 +124,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi 
         elif [ ${slim_trainer} = "fpgm" ]; then
-            if [ ${model_name} = "det" ]; then
+            if [ ${model_name} = "ocr_det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../

test/test.sh

@@ -110,7 +110,7 @@ function func_inference(){
                 for threads in ${cpu_threads_list[*]}; do
                     for batch_size in ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -124,7 +124,7 @@ function func_inference(){
                     fi
                     for batch_size in ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -138,9 +138,9 @@ if [ ${MODE} != "infer" ]; then
 
 IFS="|"
 for gpu in ${gpu_list[*]}; do
-    use_gpu=True
+    train_use_gpu=True
     if [ ${gpu} = "-1" ];then
-        use_gpu=False
+        train_use_gpu=False
         env=""
     elif [ ${#gpu} -le 1 ];then
         env="export CUDA_VISIBLE_DEVICES=${gpu}"
@@ -181,7 +181,7 @@ for gpu in ${gpu_list[*]}; do
 
             save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
             if [ ${#gpu} -le 2 ];then  # epoch_num #TODO
-                cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
+                cmd="${python} ${run_train} ${train_use_gpu_key}=${train_use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
             elif [ ${#gpu} -le 15 ];then
                 cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num}  ${save_model_key}=${save_log}"
             else

tools/infer/predict_det.py

@@ -106,7 +106,7 @@ class TextDetector(object):
                 model_precision=args.precision,
                 batch_size=1,
                 data_shape="dynamic",
-                save_path="./output/auto_log.lpg",
+                save_path=args.save_log_path,
                 inference_config=self.config,
                 pids=pid,
                 process_name=None,
@@ -174,7 +174,7 @@ class TextDetector(object):
         data = {'image': img}
 
         st = time.time()
-        
+
         if args.benchmark:
             self.autolog.times.start()
 
@@ -212,7 +212,7 @@ class TextDetector(object):
         else:
             raise NotImplementedError
 
-        self.predictor.try_shrink_memory()
+        #self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
         if self.det_algorithm == "SAST" and self.det_sast_polygon:
@@ -262,7 +262,6 @@ if __name__ == "__main__":
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
         logger.info("The visualized image saved in {}".format(img_path))
-    
+
     if args.benchmark:
         text_detector.autolog.report()
-

tools/infer/utility.py

@@ -176,6 +176,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_59.tmp_0": [1, 96, 20, 20],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
+                "conv2d_124.tmp_0": [1, 96, 20, 20],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
                 "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
@@ -188,6 +189,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_91.tmp_0": [1, 96, 200, 200],
                 "conv2d_59.tmp_0": [1, 96, 400, 400],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
+                "conv2d_124.tmp_0": [1, 256, 400, 400],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
@@ -202,6 +204,7 @@ def create_predictor(args, mode, logger):
                 "conv2d_59.tmp_0": [1, 96, 160, 160],
                 "nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
                 "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
+                "conv2d_124.tmp_0": [1, 256, 160, 160],
                 "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
                 "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
                 "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
@@ -237,7 +240,7 @@ def create_predictor(args, mode, logger):
 
     # enable memory optim
     config.enable_memory_optim()
-    config.disable_glog_info()
+    #config.disable_glog_info()
 
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
     if mode == 'table':